Quantum interference in a superconductor-MnBi2Te4{\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}-superconductor Josephson junction

We study the transport properties of a Josephson junction consisting of two identical $s$-wave superconductors separated by an even-layer ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ (MBT). Using recursive Green's function method, we calculate the supercurrent in the presence of a perpendicular magnetic field and find that its quantum interference exhibits distinct patterns when the MBT is in different magnetic states. In the antiferromagnetic state, the MBT is an axion insulator supporting an extended "hinge" supercurrent, which leads to a sinusoidal interference pattern decaying with the field strength. In the ferromagnetic state, the MBT is a Chern insulator and the unbalanced chiral supercurrents on opposite edges give rise to a highly asymmetric interference pattern. If the MBT turns into a metal as the Fermi level is tuned into the conduction band, the interference exhibits a Fraunhofer pattern due to the uniformly distributed bulk supercurrent. Our work unravels a strong indicator to identify different phases in the MBT and can be verified directly by experiments

Numerical simulation of hydrodynamics and reaeration over a stepped spillway by the SPH method

Aerated flows are characterized by complex hydrodynamics and mass-transfer processes. As a Lagrangian method, smoothed particle hydrodynamics (SPH) has a significant advantage in tracking the air-water interface in turbulent flows. This paper presents the application of an SPH method to investigate hydrodynamics and reaeration over stepped spillways. In the SPH method, the entrainment of dissolved oxygen (DO) is studied using a multiphase mass transfer SPH method for reaeration. The numerical results are compared with the hydrodynamics data from Chanson and DO data from Cheng. The simulation results show that velocity distribution and the location of free-surface aeration inception agree with the experimental results. Compared with the experimental results, the distribution of DO concentration over the stepped spillway is consistent with the measurement results. The study shows that the two-phase DO mass transfer SPH model is reliable and reasonable for simulating the hydrodynamics characteristics and reaeration process

A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta-learning based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

Effects on the pore structure and permeability change by coke deposition during crude oil in-situ combustion

In-situ combustion(ISC) is an enhanced oil recovery technique to exploit the unconventional crude oil resources with high recovery efficiency. Great amount of reaction heat is released in-place by burning the solid residue, so-called coke at the combustion front with the temperature higher than 400℃. Significant open ISC questions include the effect of coke formation on the pore structure and permeability. Coke deposition reduces the permeability and increases the permeability heterogeneities which will affect the oxygen transport in the formation, thereby influencing the oxygen participating reactions downstream. However, the existing empirical or semi-empirical relationships are still questionable to model the permeability change due to coke deposition. In the study, a high temperature and high pressure experimental apparatus was constructed to physically simulate the coke formation during the ISC processes. Please download the full abstract below

Variational generation of spin squeezing on one-dimensional quantum devices with nearest-neighbor interactions

Efficient preparation of spin-squeezed states is important for quantum-enhanced metrology. Current protocols for generating strong spin squeezing rely on either high dimensionality or long-range interactions. A key challenge is how to generate considerable spin squeezing in one-dimensional systems with only nearest-neighbor interactions. Here, we develop variational spin-squeezing algorithms to solve this problem. We consider both digital and analog quantum circuits for these variational algorithms. After the closed optimization loop of the variational spin-squeezing algorithms, the generated squeezing can be comparable to the strongest squeezing created from two-axis twisting. By analyzing the experimental imperfections, the variational spin-squeezing algorithms proposed in this work are feasible in recent developed noisy intermediate-scale quantum computers

Supramolecular Assembly of Tetramethylcucurbit[6]uril and 2-Picolylamine

The supramolecular assembly of symmetrical tetramethylcucurbit[6]uril (TMeQ[6]) and 2-picolylamine (AMPy) has been investigated via various techniques, including ultraviolet-visible (UV-vis) and nuclear magnetic resonance spectroscopy, isothermal titration calorimetry (ITC), and X-ray crystallography. The results indicated that TMeQ[6] could encapsulate the AMPy guest molecule to form a stable inclusion complex. The rotational restriction of the guest in the cavity of TMeQ[6] resulted in a large negative value of entropy. The X-ray crystal structure of the 1:1 inclusion complex between TMeQ[6] and AMPy revealed that AMPy exists in the elliptical cavity of TMeQ[6]